README.md

---
pipeline_tag: text-to-image
widget:
- text: >-
    black fluffy gorgeous dangerous cat animal creature, large orange eyes, big
    fluffy ears, piercing gaze, full moon, dark ambiance, best quality,
    extremely detailed
  output:
    url: assets/final_output_00975_.png
- text: >-
    (impressionistic realism by csybgh), a 50 something male, working in
    banking, very short dyed dark curly balding hair, Afro-Asiatic ancestry,
    talks a lot but listens poorly, stuck in the past, wearing a suit, he has a
    certain charm, bronze skintone, sitting in a bar at night, he is smoking and
    feeling cool, drunk on plum wine, masterpiece, 8k, hyper detailed, smokey
    ambiance, perfect hands AND fingers
  output:
    url: assets/final_output_00980_.png
- text: >-
    high quality pixel art, a pixel art silhouette of an anime space-themed girl
    in a space-punk steampunk style, lying in her bed by the window of a
    spaceship, smoking, with a rustic feel. The image should embody epic
    portraiture and double exposure, featuring an isolated landscape visible
    through the window. The colors should primarily be dynamic and
    action-packed, with a strong use of negative space. The entire artwork
    should be in pixel art style, emphasizing the characters shape and set
    against a white background. Silhouette
  output:
    url: assets/final_output_00817_.png
- text: >-
    The image features an older man, a long white beard and mustache,  He has a
    stern expression, giving the impression of a wise and experienced
    individual. The mans beard and mustache are prominent, adding to his
    distinguished appearance. The close-up shot of the mans face emphasizes his
    facial features and the intensity of his gaze.
  output:
    url: assets/final_output_00987_.png
- text: >-
    Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass
    flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green
    used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty,
    noisy, Vintage monk style, very detailed, hd
  output:
    url: assets/final_output_00813_.png
- text: >-
    cinematic film still of Kodak Motion Picture Film: (Sharp Detailed Image) An
    Oscar winning movie for Best Cinematography a woman in a kimono standing on
    a subway train in Japan Kodak Motion Picture Film Style, shallow depth of
    field, vignette, highly detailed, high budget, bokeh, cinemascope, moody,
    epic, gorgeous, film grain, grainy
  output:
    url: assets/final_output_00991_.png
- text: >-
    1980s anime portrait of a character 
  output:
    url: assets/final_output_00994_.png
license: apache-2.0
---
<Gallery />

# PrometheusV1
PrometheusV1 is presumed to be the first full rank finetune of Playground v2.5, developed by the creator of the Proteus model. This text-to-image generation model has been specifically adapted to enhance accessibility for the open-source community.

# Key Features and Considerations

  Presumed First Full Rank Finetune of Playground v2.5:
    
    Complete parameter update of Playground v2.5 architecture
    Unique approach to fine-tuning this particular base model
    
  
  Enhanced Accessibility:
    
    Custom sampling methods have been removed through brute force techniques
    Designed to be more compatible with standard open-source tools and workflows
    post processing applied to ensure backwards compatibility with most SDXL LoRAs and tools
    
    
  Output Characteristics:
    
    Aims to provide a balance between consistency and diversity in outputs
    May exhibit some stylistic tendencies inherited from the training process
    
  
  Training Approach:
    
    Utilizes the extensive Proteus datasets of over 400,000 images
    Brute force at scale training methodology
    Focused on maintaining model capabilities while increasing compatibility
    
  
  Custom CLIP Integration:
  
    Incorporation of a specially trained CLIP model
    Requires a clip skip setting of 2 for optimal performance



# About PrometheusV1
PrometheusV1 represents a significant effort to make advanced text-to-image generation more accessible to the open-source community. Built upon the Playground v2.5 architecture, it has undergone a full rank finetune using an extensive dataset of over 400,000 images from the Proteus collection.
A key aspect of its development was the removal of custom sampling methods through brute force techniques at scale, allowing the model to work more seamlessly with standard open-source tools and pipelines. Additionally, PrometheusV1 has been made backwards compatible with most SDXL LoRAs and tools.
This approach aims to balance the model's performance capabilities with wider compatibility and ease of use. Users can expect outputs that reflect the model's intensive training on the large Proteus dataset while benefiting from improved interoperability with common open-source frameworks and existing SDXL ecosystem.

# Training Details

Base Model: Playground v2.5
Finetune Type: Full rank (all layers updated)
Training Dataset: Over 400,000 images from Proteus datasets, extensively curated and processed
Training Approach: Brute force at scale, focused on removing custom sampling methods while maintaining model capabilities
Fine-tuning Techniques: Standard optimization methods compatible with open-source tools
Special Processing: post processing applied for SDXL LoRA and tool compatibility

# Recommended Settings

Clip Skip: 2
CFG Scale: 7
Steps: 25 - 50
Sampler: DPM++ 2M SDE
Scheduler: Karras
Resolution: 1024x1024

# Use it with 🧨 diffusers
```python
import torch
from diffusers import (
    StableDiffusionXLPipeline, 
    KDPM2AncestralDiscreteScheduler,
    AutoencoderKL
)

# Load VAE component
vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", 
    torch_dtype=torch.float16
)

# Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "dataautogpt3/PrometheusV1", 
    vae=vae,
    torch_dtype=torch.float16
)
pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')

# Define prompts and generate image
prompt = "a cat wearing sunglasses on the beach"
negative_prompt = ""

image = pipe(
    prompt, 
    negative_prompt=negative_prompt, 
    width=1024,
    height=1024,
    guidance_scale=7,
    num_inference_steps=50,
    clip_skip=2
).images[0]


image.save("generated_image.png")
```